Image matching techniques may be used in a variety of applications such as control of industrial processes, tracking, detecting events, organizing or retrieving image data, and object or place recognition.
The effectiveness of object recognition may depend on the image matching algorithm that is used by an object recognition process. An image matching algorithm may utilize a computed parameter such as a descriptor of a digital image for use by the recognition process. A descriptor of a digital image, for example, may refer to characteristics and/or features of an image. Descriptors may also be local and need not describe an entire image or an object in an image. Descriptors for different images may be compared using a variety of distance metrics to find matching regions in other images.
Some objects and landmarks in images have regular patterns. For example, grids, checkerboards and windows in buildings are regular repeating patterns. Such patterns are likely to produce false matches when using local descriptors for object recognition. Lacking distinctiveness, these patterns tend to form clusters of local point matches between similar structures that are geometrically consistent. This consistency may lead to high matching confidence scores even though the images are a mismatch.
Systems and methods for visual object recognition are needed that reduce false matches and improve performance of an image matching process as compared to present methods.